Statistical calibration of probabilistic medium-range Fire Weather Index forecasts in Europe

Abstract

Wildfires are increasing in frequency and severity across Europe, which makes accurate wildfire risk estimation crucial for decision-makers and emergency responders. Wildfire risk is usually estimated using meteorological-based fire weather indices such as the Canadian Forest Fire Weather Index (FWI). By using weather forecasts, the FWI can be predicted for several days and even weeks ahead. Probabilistic ensemble forecasts require verification and calibration in order to provide reliable and accurate forecasts, which are crucial for informed decision-making and an effective emergency response. In this study, we investigate the potential of non-homogeneous Gaussian regression (NGR) for statistically calibrating ensemble forecasts of the FWI. The FWI is calculated using medium-range ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) with lead times up to 15 d over Europe. The method is tested using a 30 d rolling training period and dividing the European region into three training areas (northern, central, and Mediterranean Europe). The calibration improves FWI forecast particularly at shorter lead times up to 84 h and in regions with elevated FWI values, i.e. areas with a higher wildfire risk such as central and Mediterranean Europe. The study demonstrates that NGR can be used to improve probabilistic FWI forecasts especially in the time range most critical for firefighting resource management and thereby supporting effective wildfire response strategies.